Please use this identifier to cite or link to this item:
|Title:||Online PCA for contaminated data||Authors:||Feng, J.
|Issue Date:||2013||Citation:||Feng, J.,Xu, H.,Mannor, S.,Yan, S. (2013). Online PCA for contaminated data. Advances in Neural Information Processing Systems. ScholarBank@NUS Repository.||Abstract:||We consider the online Principal Component Analysis (PCA) where contaminated samples (containing outliers) are revealed sequentially to the Principal Components (PCs) estimator. Due to their sensitiveness to outliers, previous online PCA algorithms fail in this case and their results can be arbitrarily skewed by the outliers. Here we propose the online robust PCA algorithm, which is able to improve the PCs estimation upon an initial one steadily, even when faced with a constant fraction of outliers. We show that the final result of the proposed online RPCA has an acceptable degradation from the optimum. Actually, under mild conditions, online RPCA achieves the maximal robustness with a 50% breakdown point. Moreover, online RPCA is shown to be efficient for both storage and computation, since it need not re-explore the previous samples as in traditional robust PCA algorithms. This endows online RPCA with scalability for large scale data.||Source Title:||Advances in Neural Information Processing Systems||URI:||http://scholarbank.nus.edu.sg/handle/10635/84041||ISSN:||10495258|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 20, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.